Qwen Image 2512 vs Flux Dev: Budget Showdown at $0.003

Overall Scores

These are the two cheapest models in our 18-model benchmark, both at $0.003 per image. Qwen ranks 12th and Flux Dev 15th — solidly mid-pack. The 0.095 gap between them is moderate but consistent. Both completed all 200 benchmark prompts with no content restrictions.

Average weighted score across 200 prompts. Both models completed all prompts.

Dimension-by-Dimension Breakdown

The dimensions split 2-2. Flux Dev leads visual fidelity (4.65 vs 4.57) and physics & logic (4.14 vs 3.87) — the latter being the largest gap at +0.27. Qwen takes subject & object integrity (4.12 vs 3.95, +0.17). Instruction adherence is essentially a dead heat (3.86 vs 3.85).

These averages mask significant prompt-level variation. Despite splitting dimensions evenly, Qwen wins 104 of 200 prompts vs Flux Dev's 76 — its subject integrity advantage shows up more consistently across prompt types.

Where Model Choice Matters Most

At identical pricing, this comparison is purely about capability. Below are six use cases where the models diverge most — three where Qwen dominates and three where Flux Dev takes the lead. The score gaps on individual prompts can exceed 1.5 points.

Stylized & cartoon illustration

Qwen wins 5 of 8 character design prompts, Flux wins 3

prompt-0090

“Cartoon frog doing a handstand, wobbly arms, Cuphead style”

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Qwen Image 2512

4.26

Biomechanics 4/5 — wobbly arm posture captures the cartoon physics well. Aesthetics 5/5 — clean Cuphead-era rubber hose style with appropriate thick outlines and saturated colors. Semantic accuracy 5/5 — frog, handstand, wobbly arms all present.

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Flux Dev

2.83

Biomechanics 2/5 — stiff posture that doesn't read as "wobbly." Aesthetics 3/5 — style is closer to generic cartoon than Cuphead specifically. Semantic accuracy 3/5 — handstand is present but the character lacks the rubber hose charm requested.

Qwen has a natural affinity for stylized illustration — it captured both the Cuphead aesthetic and the playful “wobbly” physics. Flux Dev produced a more generic cartoon that missed the style reference.

Dynamic action & sports

Flux wins most dynamic human pose prompts — the largest gap in this benchmark

prompt-0088

“Dramatic slow-motion capture of a goalkeeper diving to save a penalty kick, body fully horizontal in mid-air with arms outstretched, fingertips...”

Show full prompt

?

Flux Dev

4.56

Biomechanics 5/5 — body fully horizontal, arms outstretched naturally, knee bent as specified. Material physics 5/5 — rain streaks catching backlight, mud spray from pitch. Imaging quality 5/5 — shallow depth of field with rim lighting. Subject integrity 4/5 — anatomically sound goalkeeper in mid-dive.

?

Qwen Image 2512

2.73

Biomechanics 2/5 — stiff pose that doesn't convey mid-air momentum. The body isn't fully horizontal and the dive looks posed rather than dynamic. Imaging quality 3/5 — lighting is flat compared to the rim light specification. Subject integrity 3/5 — anatomy is acceptable but the motion freeze is unconvincing.

The largest gap in this benchmark — 1.83 points. Flux Dev excelled at the frozen-motion effect with convincing mid-air physics. Qwen produced a stiff, posed-looking image that didn't capture the dynamic energy of the scene.

Portrait & facial detail

Flux wins 3 of 5 portrait prompts, Qwen wins 2

prompt-0027

“Portrait showing both ears of person facing camera directly, symmetrical face, neutral expression”

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Qwen Image 2512

4.53

Subject integrity 5/5 — symmetrical face with both ears clearly visible, natural proportions. Instruction adherence 5/5 — neutral expression, direct camera gaze, both ears showing. Visual fidelity 4/5 — clean rendering with good skin detail.

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Flux Dev

3.15

Subject integrity 3/5 — face is slightly asymmetric, one ear partially obscured by hair. Instruction adherence 3/5 — doesn't fully satisfy the "both ears" and "symmetrical" requirements. Visual fidelity 3/5 — softer detail and less controlled lighting.

On this specific portrait, Qwen followed the symmetry and ear requirements precisely. Flux Dev partially obscured one ear — a common failure when models don't prioritize the prompt's specific constraints. However, Flux Dev wins more portrait prompts overall.

Still life & dramatic lighting

Flux wins 3 of 5 food/beverage prompts, Qwen wins 2

prompt-0012

“Wine glass balanced on the edge of a table, red wine inside, dramatic lighting”

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Flux Dev

4.85

Static physics 5/5 — wine glass correctly balanced on table edge with plausible center of gravity. Material physics 5/5 — wine liquid level and glass refraction are optically accurate. Imaging quality 5/5 — dramatic lighting with clean specular highlights on glass. Aesthetics 5/5.

?

Qwen Image 2512

3.61

Static physics 3/5 — glass placement feels less precarious than "balanced on the edge" implies. Material physics 3/5 — wine level and glass refraction are less refined. Imaging quality 4/5 — lighting is dramatic but lacks the specular precision. Aesthetics 4/5.

Flux Dev rendered the precarious balance and glass refraction with photographic precision. Qwen's version is competent but lacks the optical accuracy and dramatic tension that makes this image work.

Physics puzzles

Qwen wins most physics-challenge prompts involving structural balance

prompt-0015

“House of cards three levels high on a table”

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Qwen Image 2512

3.53

Static physics 4/5 — three-level card structure with plausible lean angles between cards. Semantic accuracy 4/5 — three levels as requested. Object integrity 3/5 — cards have consistent sizing and playing card appearance.

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Flux Dev

2.29

Static physics 2/5 — card arrangement doesn't form a stable three-level structure. Semantic accuracy 2/5 — number of levels is ambiguous. Object integrity 2/5 — cards appear warped and inconsistent in size.

Both models found this challenging — neither scored above 3.53. But Qwen produced a more structurally plausible card house with the correct number of levels. Flux Dev's arrangement didn't form a recognizable three-level structure.

Fashion editorial

Flux wins 3 of 5 fashion prompts, Qwen wins 2

prompt-0109

“High fashion editorial photograph of a model emerging from a swimming pool at twilight, water cascading off a metallic gold lame gown that clings to...”

Show full prompt

?

Flux Dev

4.04

Material physics 4/5 — wet gold lame fabric drapes convincingly with visible weight change from water. Imaging quality 5/5 — split lighting scheme is well executed. Subject integrity 4/5 — model anatomy and pose are natural. Aesthetics 4/5 — captures the Tim Walker editorial aesthetic.

?

Qwen Image 2512

2.91

Material physics 2/5 — fabric doesn't convincingly show wet vs dry behavior. Imaging quality 3/5 — lighting scheme is flat compared to the split lighting requested. Subject integrity 3/5 — model rendering is less refined. Aesthetics 3/5 — doesn't capture the high-fashion editorial feel.

Fashion editorial demands material physics (wet fabric drape), lighting precision (split color gels), and overall aesthetic coherence. Flux Dev delivered on all three; Qwen's version felt flat and lacked the editorial energy.

Prompt-Level Results

Across all 200 prompts, Qwen Image 2512 wins 104 while Flux Dev wins 76 — with 20 ties. Qwen's lead is comfortable but Flux Dev wins enough prompts (38%) to be the better choice in specific contexts.

A “win” is defined as a score difference greater than 0.01 on a given prompt.

The Budget Value Proposition

At $0.003 per image, both models are 10-45x cheaper than mid-tier alternatives. To put that in perspective: you can generate 44 images with either model for the cost of one GPT Image 1.5 generation. The quality tradeoff is surprisingly small.

Qwen delivers 92% of GPT Image 1.5's quality at 2.3% of the price. That's the budget sweet spot — the point where quality degradation is minimal but cost savings are massive.

Strengths and Limitations

The Verdict

Related Benchmarks

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